---
license: mit
---
# Vietnamese Legal Text BERT
#### Table of contents
1. [Introduction](#introduction)
2. [Using Vietnamese Legal Text BERT](#transformers)
	- [Installation](#install2)
	- [Pre-trained models](#models2)
	- [Example usage](#usage2)

# <a name="introduction"></a> Using Vietnamese Legal Text BERT `hmthanh/VietnamLegalText-SBERT`

Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam): 


## <a name="transformers"></a> Using Vietnamese Legal Text BERT `transformers`

### Installation <a name="install2"></a>
- Install `transformers` with pip:
`pip install transformers`<br />

- Install `tokenizers` with pip:
`pip install tokenizers`

### Pre-trained models <a name="models2"></a>


Model | #params | Arch.	 | Max length | Pre-training data
---|---|---|---|---
`hmthanh/VietnamLegalText-SBERT` | 135M | base | 256 | 20GB  of texts

### Example usage <a name="usage2"></a>

```python
import torch
from transformers import AutoModel, AutoTokenizer

phobert = AutoModel.from_pretrained("hmthanh/VietnamLegalText-SBERT")
tokenizer = AutoTokenizer.from_pretrained("hmthanh/VietnamLegalText-SBERT")

sentence = 'Chúng_tôi là những nghiên_cứu_viên .'  

input_ids = torch.tensor([tokenizer.encode(sentence)])

with torch.no_grad():
    features = phobert(input_ids)  # Models outputs are now tuples
```